Electronic mail (email) is an established form of communication used ubiquitously by individuals in business and private sectors. An email user may receive a large number of inbound email messages in a given day, which are traditionally delivered to a single folder (typically referred to as the “Inbox”) in the user's email system. To identify those messages that the user considers important, the user must review each message in the Inbox, and must manually delete messages that are not considered to be important in order to remove them from the Inbox. Such systems are therefore inefficient in that they require the user to spend a considerable amount of time managing inbound email.
To improve on such traditional systems, some modern email systems provide “filters” which act on (process) inbound messages. Such filters may be based on rules established by the user which define a set of actions to be performed on inbound messages. For example, the user may create a rule whereby any message containing the phrase “Special Offer” is filtered by the system such that it is delivered to a folder intended to contain unimportant messages (e.g. a “Spam” folder). Depending on the system, the message may be automatically moved to the Spam folder upon delivery to the Inbox, or it may bypass the Inbox and be delivered directly to the Spam folder.
While such rule-based-filters provide the user with precise control over inbound messages, they can be time-consuming to create and maintain. Moreover, rule-based filters will not filter messages that do not match a rule previously established by the user, requiring the user to manually review and act on such messages.
To overcome the limitations of rule-based filters, some email systems now include “predictive” filters. Predictive filters use fuzzy logic to determine the action that should be taken on inbound messages, on the basis of message information and a priori information that is stored in the system. Thus, predictive filters are capable of operating without user intervention and may also capable of acting on messages from unknown recipients. For example, email systems are known that rank inbound messages based on metrics derived from earlier user actions to related messages (e.g. Gmail's™ Priority Inbox™ feature; U.S. Published Application No. US20060235933; U.S. Pat. No. 8,095,612).
Some email systems that rank/prioritize email may rely on a specific email client having a custom User Interface (UI) to visually separate important from messages from unimportant messages. Such email systems have a server and client parts which are designed to be used together and thus may be incompatible, or provide degraded operation if a third-part email client is used. For example, Priority Inbox™ provides a custom web UI and mobile application UI (e.g. U.S. Pat. No. 8,312,096) to visually separate a user's Inbox into two or more distinct groups, such as “Important” messages and “Everything Else.” However, if a third-party email client is used in conjunction with Gmail's™ email server, such a visual separation between important and unimportant messages may be lost.
While rule-based filters and predictive filters offer increased efficiency in managing email, they are not included in all email systems. Further, many users currently use legacy email systems that lack one or more of the features described above. Some users may be reluctant to switch email systems because of the time and effort involved. Other users may be required by their employer to use a legacy email system.
Thus, there is a need in the art for improved email filtering systems, in particular systems which may be used universally, which are compatible with existing email servers and clients, and which may be customized and/or expanded upon to meet a user's specific needs.